zkml-blueprints

A growing collection of custom circuit designs—with every constraint proven and every idea explained.
Built for clarity, rigor, and community collaboration—the reference we all wish we’d had!

zkml-blueprints is a collection of mathematical formulations and circuit designs supporting zero-knowledge (ZK) proofs for machine learning (ML) applications, focusing on efficiently implementing provable computations in ZK circuits. This repository provides formal descriptions, constraints, and structured blueprints for designing circuits that preserve privacy while ensuring verifiable correctness.

If you're new here, notes on constraint system design is a good starting point. In fact the represent the beginning of an extensive revision of this repo. In the meantime, other docs may still be useful.

Features

• 📖 Mathematical Formulations – Rigorous explanations of key principles underlying ZK circuit design.
• 🛠 Circuit Constraints – Well-documented approaches to encoding computations in finite fields.
• ⚡ Implementation Guidelines – Blueprints for translating mathematical constructs into efficient circuit implementations.
• 🔒 Use Cases – Applications in cryptographic protocols, zk-SNARKs, and privacy-preserving machine learning.

This repository aims to serve as a structured reference for researchers, developers, and practitioners working on ZK and zkML proof systems, offering both theoretical insights and practical implementations.

💡 Contributions and discussions are welcome!

📁 Repository Structure

Each subdirectory focuses on a specific class of operations:

Each section contains:

• 📄 PDF blueprints describing the circuit logic, constraints, and mathematical justification
• 📓 (Planned) Jupyter notebooks for live examples and testing
• 📘 A local README.md with context and links